From Static to Dynamic Anomaly Detection with Application to Power System Cyber Security

Abstract

Developing advanced diagnosis tools to detect cyber attacks is the key to security of power systems. It has been shown that multivariate data injection attacks can bypass bad data detection schemes typically built on static behavior of the systems, which misleads operators to disruptive decisions. In this article, we depart from the existing static viewpoint to develop a diagnosis filter that captures the dynamics signatures of such a multivariate intrusion. To this end, we introduce a dynamic residual generator approach formulated as robust optimization programs in order to detect a class of disruptive multivariate attacks that potentially remain stealthy in view of a static bad data detector. We investigate two possible desired features: (i) a non-zero transient and (ii) a non-zero steady-state behavior of the residual generator in the presence of an attack. In case (i), the problem is reformulated as a finite, but possibly non-convex, optimization program. We further develop a linear programming relaxation that improves the scalability, and as such practicality, of the diagnosis filter design. In case (ii), it turns out that the resulting robust program admits an exact convex reformulation, yielding a Nash equilibrium between the attacker and the residual generator. This assertion has an interesting implication: the proposed approach is not conservative in the sense that the additional knowledge of the worst-case attack does not improve the diagnosis performance. To illustrate our theoretical results, we implement the proposed diagnosis filter to detect multivariate attacks on the system measurements deployed to generate the so-called Automatic Generation Control signals in a three-area IEEE 39-bus system.

References in corpus (4)

• Vulnerability Analysis and Consequences of False Data Injection Attack on Power System State Estimation
• On the Existence of a Kazantzis-Kravaris/Luenberger Observer
• A Tractable Fault Detection and Isolation Approach for Nonlinear Systems with Probabilistic Performance
• From Static to Dynamic Anomaly Detection with Application to Power System Cyber Security

Cited by in corpus (5)

• From Static to Dynamic Anomaly Detection with Application to Power System Cyber Security
• Detection of False Data Injection Attacks Using the Autoencoder Approach
• Training Strategies for Autoencoder-based Detection of False Data Injection Attacks
• Dynamic Anomaly Detection with High-fidelity Simulators: A Convex Optimization Approach
• False Data Injection Attacks on Hybrid AC/HVDC Interconnected System with Virtual Inertia -- Vulnerability, Impact and Detection